{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用户和活动关联关系处理\n",
    "\n",
    "\n",
    "整个数据集中活动数目（events.csv）太多，所以下面的处理我们找出只在训练集和测试集中出现的活动和用户集合，并对他们重新编制索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#保存数据\n",
    "import pickle\n",
    "import csv\n",
    "import itertools\n",
    "\n",
    "#处理事件字符串\n",
    "import datetime\n",
    "\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import scipy.sparse as ss\n",
    "import pandas as pd\n",
    "\n",
    "#相似度/距离\n",
    "import scipy.spatial.distance as ssd\n",
    "\n",
    "from collections import defaultdict\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of uniqueUsers :3391\n",
      "number of uniqueEvents :13418\n"
     ]
    }
   ],
   "source": [
    " \"\"\"\n",
    "我们只关心train和test中出现的user和event，因此重点处理这部分关联数据\n",
    "\n",
    "train.csv 有6列：\n",
    "user：用户ID\n",
    "event：活动ID\n",
    "invited：是否被邀请（0/1）\n",
    "timestamp：ISO-8601 UTC格式时间字符串，表示用户看到该活动的时间\n",
    "interested, and not_interested\n",
    "\n",
    "Test.csv 除了没有interested, and not_interested，其余列与train相同\n",
    " \"\"\"\n",
    "    \n",
    "# 统计训练集中有多少不同的用户的events\n",
    "uniqueUsers = set()\n",
    "uniqueEvents = set()\n",
    "\n",
    "#倒排表\n",
    "#统计每个用户参加的活动   / 每个活动参加的用户\n",
    "eventsForUser = defaultdict(set)\n",
    "usersForEvent = defaultdict(set)\n",
    "    \n",
    "for filename in [\"train.csv\", \"test.csv\"]:\n",
    "    f = open(filename, 'rb')\n",
    "    \n",
    "    #忽略第一行（列名字）\n",
    "    f.readline().strip().decode().split(\",\")\n",
    "    \n",
    "    for line in f:    #对每条记录\n",
    "        cols = line.strip().decode().split(\",\")\n",
    "        uniqueUsers.add(cols[0])   #第一列为用户ID\n",
    "        uniqueEvents.add(cols[1])   #第二列为活动ID\n",
    "        \n",
    "        #eventsForUser[cols[0]].add(cols[1])    #该用户参加了这个活动\n",
    "        #usersForEvent[cols[1]].add(cols[0])    #该活动被用户参加\n",
    "    f.close()\n",
    "\n",
    "\n",
    "n_uniqueUsers = len(uniqueUsers)\n",
    "n_uniqueEvents = len(uniqueEvents)\n",
    "\n",
    "print(\"number of uniqueUsers :%d\" % n_uniqueUsers)\n",
    "print(\"number of uniqueEvents :%d\" % n_uniqueEvents)\n",
    "\n",
    "#用户关系矩阵表，可用于后续LFM/SVD++处理的输入\n",
    "#这是一个稀疏矩阵，记录用户对活动感兴趣\n",
    "userEventScores = ss.dok_matrix((n_uniqueUsers, n_uniqueEvents))\n",
    "userIndex = dict()\n",
    "eventIndex = dict()\n",
    "\n",
    "#重新编码用户索引字典\n",
    "for i, u in enumerate(uniqueUsers):\n",
    "    userIndex[u] = i\n",
    "    \n",
    "#重新编码活动索引字典    \n",
    "for i, e in enumerate(uniqueEvents):\n",
    "    eventIndex[e] = i\n",
    "\n",
    "n_records = 0\n",
    "ftrain = open(\"train.csv\", 'rb')\n",
    "ftrain.readline()\n",
    "for line in ftrain:\n",
    "    cols = line.strip().decode().split(\",\")\n",
    "    i = userIndex[cols[0]]  #用户\n",
    "    j = eventIndex[cols[1]] #活动\n",
    "    \n",
    "    eventsForUser[i].add(j)    #该用户参加了这个活动\n",
    "    usersForEvent[j].add(i)    #该活动被用户参加\n",
    "        \n",
    "    #userEventScores[i, j] = int(cols[4]) - int(cols[5])   #interested - not_interested\n",
    "    score = int(cols[4])\n",
    "    #if score == 0:  #0在稀疏矩阵中表示该元素不存在，因此借用-1表示interested=0\n",
    "    #userEventScores[i, j] = -1s \n",
    "    #else:\n",
    "    userEventScores[i, j] = score\n",
    "ftrain.close()\n",
    "\n",
    "  \n",
    "##统计每个用户参加的活动，后续用于将用户朋友参加的活动影响到用户\n",
    "pickle.dump(eventsForUser, open(\"PE_eventsForUser.pkl\", 'wb'))\n",
    "\n",
    "##统计活动参加的用户\n",
    "pickle.dump(usersForEvent, open(\"PE_usersForEvent.pkl\", 'wb'))\n",
    "\n",
    "#保存用户-活动关系矩阵R，以备后用\n",
    "sio.mmwrite(\"PE_userEventScores\", userEventScores)\n",
    "\n",
    "\n",
    "#保存用户索引表\n",
    "pickle.dump(userIndex, open(\"PE_userIndex.pkl\", 'wb'))\n",
    "#保存活动索引表\n",
    "pickle.dump(eventIndex, open(\"PE_eventIndex.pkl\", 'wb'))\n",
    "\n",
    "    \n",
    "# 为了防止不必要的计算，我们找出来所有关联的用户 或者 关联的event\n",
    "# 所谓的关联用户，指的是至少在同一个event上有行为的用户pair\n",
    "# 关联的event指的是至少同一个user有行为的event pair\n",
    "uniqueUserPairs = set()\n",
    "uniqueEventPairs = set()\n",
    "for event in uniqueEvents:\n",
    "    i = eventIndex[event]\n",
    "    users = usersForEvent[i]\n",
    "    if len(users) > 2:\n",
    "        uniqueUserPairs.update(itertools.combinations(users, 2))\n",
    "        \n",
    "for user in uniqueUsers:\n",
    "    u = userIndex[user]\n",
    "    events = eventsForUser[u]\n",
    "    if len(events) > 2:\n",
    "        uniqueEventPairs.update(itertools.combinations(events, 2))\n",
    " \n",
    "#保存用户-事件关系对索引表\n",
    "pickle.dump(uniqueUserPairs, open(\"FE_uniqueUserPairs.pkl\", 'wb'))\n",
    "pickle.dump(uniqueEventPairs, open(\"PE_uniqueEventPairs.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  将Train 和 Test中的Events ID 保存到文件 UniqueEvents.csv中\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     "text": [
      "<class 'str'>\n",
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     "text": [
      "<class 'str'>\n",
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     "text": [
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     "text": [
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     "text": [
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     "text": [
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     "text": [
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     "text": [
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     "text": [
      "<class 'str'>\n",
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      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# print(uniqueEvents)\n",
    "with open('UniqueEvents.csv','w') as f:\n",
    "    w = csv.writer(f)\n",
    "    w.writerow(['event_id'])\n",
    "    for event in (uniqueEvents):\n",
    "        print(type(event))\n",
    "        w.writerow([event])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'52238789', '3753704467', '3429508261', '478310868', '1371415647', '3825526654', '4184715643', '1051329292', '2351154139', '3951671760', '3040691375', '1833244071', '721524089', '1386404286', '1556475107', '2146032671', '2056660430', '1771812765', '4097301015', '186107339', '110700006', '389818573', '1551227860', '530307239', '1092379167', '3769851269', '4146680584', '2240919052', '2500726913', '1487327799', '2774259276', '3739685239', '2491721612', '3434452970', '880342205', '3621868055', '3807695143', '3097220122', '856365940', '1496739876', '204809614', '1783542538', '122055810', '3013920560', '2094504098', '2776357538', '1434113959', '1329734614', '1276897840', '2888757174', '1044974227', '655841147', '626740748', '4034603808', '3931717290', '1407500515', '3166529318', '2905243784', '1190045109', '2042030195', '2045076885', '3262152241', '1499603155', '2421272892', '3224830038', '1769641333', '790991039', '3328747836', '655514048', '920954386', '3865317184', '1084665102', '153348140', '2232544430', '2750965517', '3178089116', '3629151953', '2912359758', '4102767305', '3417522749', '2088291825', '3300815276', '1202113718', '48324615', '4194316975', '2942401862', '1757688444', '3181096775', '1345381188', '581168153', '2981257562', '1738754396', '439006030', '1788118360', '3664010858', '2086304881', '25669535', '3428807031', '2533905418', '3769856229', '1709594652', '2687062464', '169396440', '56286280', '3706170359', '1532094066', '2834364568', '1583676682', '3951904788', '1191393757', '1893629654', '786670004', '995181185', '760705045', '2554767933', '2761317427', '1374408115', '199908595', '1764159783', '2979995541', '3274210826', '2379950964', '4273898950', '3302915994', '2703345903', '658933673', '3348456289', '2676920996', '447738506', '3114539041', '3335640477', '808122386', '569362828', '999770277', '3751969407', '6941167', '3538896788', '2832466526', '3574130231', '1613979400', '4338728', '3915796357', '2815362044', '3717208652', '472254159', '2468336372', '984516743', '2982417413', '2879803329', '523689598', '4019176098', '1294070735', '3260635116', '3434410911', '1630489177', '3380319047', '598096100', '3588114419', '3907154058', '2328414150', '2638437980', '1281642334', '1901044782', '3816147170', '3342919298', '2762128957', '4134671058', '2643522744', '2627346430', '235476646', '3366038267', '3596095472', '275437483', '1449780858', '1769681665', '4238847438', '1047008495', '4085386358', '2896575744', '3904821261', '2455742965', '4010042708', '1620441934', '979863610', '3966478016', '4275451050', '3862966754', '3771678348', '2233735163', '1775237825', '4177907021', '3694263991', '3961121565', '1114151918', '2740052708', '1282392038', '3072478280', '2354781149', '1414791342', '4017520480', '1864837767', '623506969', '3528953266', '1262770111', '712966241', '3203913218', '3318022618', '3879238985', '2183498812', '3658750431', '2177332500', '930199924', '3670952162', '2582794582', '1728846117', '312946086', '2473947326', '4215173213', '1795425572', '2213514822', '1742102536', '3939206437', '726631277', '2385047306', '396853098', '1148695493', '315405402', '258423677', '4126254215', '2010667544', '2133473801', '2443986891', '1904753074', '3557752126', '552686696', '40254649', '426166781', '3727482724', '2611379231', '1343362100', '2374384994', '1516890542', '2586730902', '3826292515', '3557171468', '3380687062', '2009284726', '1363299595', '3505964751', '2705765590', '2020073928', '1805938462', '2963294280', '1159558718', '3191068419', '45687644', '850932284', '1323913377', '1055406820', '2926120767', '3364940341', '2212344171', '3344442885', '3117801085', '336832383', '1401069734', '4253811572', '2572191829', '3097171667', '2518430453', '2258521927', '3784642395', '2985296458', '3012567312', '1488264230', '246977782', '568710964', '3216974136', '1498742095', '3834543367', '2724269698', '1189079895', '3572543455', '3581079888', '511414715', '2163306214', '608423375', '2324549960', '3339552268', '3883069165', '932490169', '1497201659', '2644800408', '616947433', '18401271', '2095085551', '2891447107', '330939731', '550630680', '1887085024', '491131434', '1351639930', '3801433102', '776070223', '3710884082', '591601593', '1537309411', '3476682149', '1971885970', '3774220029', '1599219405', '1520335024', '1283922964', '612644906', '1203120960', '1672084151', '1317022921', '3742929052', '2033272792', '2704232594', '2171647061', '3040656299', '2013712241', '69243713', '334013045', '1501313615', '1447651663', '1333395072', '4171753844', '1437016000', '4061182012', '613851355', '3708212410', '792632006', '740148951', '2058463423', '2357213703', '1652007005', '3321517547', '3273774165', '88942214', '1387849986', '3598553657', '3158709668', '1418616930', '518255514', '2334817798', '4210789941', '929107951', '1304281836', '3912782817', '3998504188', '495876188', '3860784194', '3623190628', '1960796894', '2503553114', '1642783054', '452879594', '3247802991', '3608285664', '3579174462', 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'2285108120', '625822979', '2186498257', '1945597998', '561980641', '1820037082', '1143203761', '3505158996', '627338344', '933991919', '3237349833', '3331152144', '1071582440', '1141674672', '2865586777', '1674888438', '1544642235', '2881298816', '1235652015', '4223848259', '2219856004', '1807134906', '1535025408', '1566051430', '2093694890', '3986920873', '1285700217', '1036819130', '2892367012', '1779648453', '600398738', '642644408', '2311572043', '858852228', '3002742471', '3504402444', '3429416155', '3229573153', '3229872217', '1605110491', '3913331948', '25062233', '2378205890', '12957499', '2881344494', '3513514173', '2808562851', '2017448857', '4088363232', '304561561', '2596820298', '150733034', '898403969', '2624001814', '1466022381', '4013344838', '4223173683', '4191471978', '3287030905', '3598658828', '1636447356', '3077993055', '4139722026', '3400642235', '125703643', '379046741', '2398475187', '1862433750', '2957317168', '3003210851', '1165693561', '960409301', '3756962861', '1555528731', '1396454425', '924928769', '2877011901', '508361018', '1646546831', '904102962', '3641477138', '2722317680', '3597169763', '3424643087', '1993102668', '485105270', '3319071521', '1168380708', '157721960', '1951237429', '2496555402', '3773519709', '1829771929', '1710609254', '3728112255', '1819998447', '1468625690', '2797343771', '843844488', '2784973713', '2433903660', '1837633909', '1073720066', '1295658867', '1396198787', '3411880387', '1499956021', '4224238432', '105718448', '1240958240', '406892307', '3327194350', '238250932', '932459990', '1320937087', '1819075340', '2625684328', '460925649', '3845010948', '559455545', '374730154', '3022995002', '3396830170', '2246530176', '4235779838', '1784859939', '1027111849', '2922629093', '4162042167', '497346227', '2055996347', '2651254812', '3294957663', '3392685835', '604416374', '236337665', '1667906928', '1965000814', '1439724705', '3827519635', '2497500261', '2869725524', '584478589', '3899724307', 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'4017891961', '498802162', '2998423233', '928621648', '2466263946', '788812553', '858488892', '1645953793', '3871984634', '3119945071', '2625858033', '1210231744', '1977805725', '491750282', '1044785443', '3720632668', '561924821', '370008848', '3946619910', '1071371108', '1462575543', '554561877', '3375794889', '639120581', '3179355340', '240233220', '1474603767', '3916699033', '522428664', '1476247853', '1777221958', '1592319948', '3221725438', '1257010649', '3201936554', '2828937719', '1522071038', '4215548909', '3479738412', '3191854902', '4134388586', '2741065012', '3905745730', '1658604508', '2822548338', '4132690541', '3076674861', '2974876918', '2924356031', '1753644287', '282104363', '714296247', '1917502195', '1413634049', '3501620432', '1644971908', '1766688413', '1500022724', '710596201', '275278465', '3218113289', '1306257783', '1711604318', '4224104772', '3064322102', '2420562513', '3656008998', '2135167144', '1654106477', '2457286435', '656598905', '37290993', '3221372597', '2190420402', '3315433656', '2915017543', '235695034', '1270918313', '2338670339', '4108357970', '4165471135', '1137820523', '1368920193', '679148240', '848453528', '1745609133', '1622766919', '2167075374', '1576680977', '2660812160', '4116657168', '112957559', '2689936663', '3504715371', '1612892907', '3943984458', '2603184455', '1409742519', '1738181791', '507118096', '3519001045', '2042835866', '4126510149', '4181313745', '3547892636', '2693701979', '2917794701', '221202458', '2647215157', '326944617', '1306609867', '3889211600', '453613647', '3151112796', '283050288', '985580109', '3003433560', '2437975546', '236692540', '3596510482', '1791133471', '3276095034', '1513616494', '3123645626', '2277283296', '3025468453', '2534283645', '2764949662', '956438727', '3686935155', '3376874101', '4116895316', '4276828707', '1799014052', '908731571', '1347202114', '1326595643', '2670146105', '1510737024', '1626858107', '3786601726', '1197761464', '197942693', '1025760152', '3472954141', '3434513569', '3944956311', '2381736875', '1247486859', '25800108', '2509477415', '1683297114', '2985301715', '144836311', '1402535842', '3319506135', '3948658168', '2025752984', '3120842736', '4294209504', '3786887622', '2940200534', '2796416513', '2625791867', '3247577480', '1421433916', '3079146363', '3491741350', '2899012651', '2458710999', '3522246891', '4286544397', '3864843980', '3891330449', '3387519234', '706941746', '725381690', '1153444608', '3606182932', '1859823732', '3245840256', '420747759', '2763980267', '1972100454', '1551986507', '55071868', '3439956212', '52639264', '856850297', '858707451', '1970404290', '259831208', '125448533', '2305935776', '126987905', '4077327097', '3482282242', '4201530293', '2650743904', '3565319586', '2793258618', '1964407998', '1404009993', '1878997696', '3428029430', '4223429256', '963733660', '4203158470', '3439842241', '3098796729', '2890950285', '2461302232', '2509151803', '4000251470', '3616067928', '2464117384', '68667425', '2273085791', '1054917549', '3628832108', '3759251777', '2741182317', '3440760913', '312761834', '3941838661', '2307691682', '2015438787', '2583046456', '1783539311', '145185489', '2997613878', '899419077', '1267918307', '1606038564', '288749001', '4144150179', '4289284578', '1494465858', '1983140495', '714110997', '4151334574', '3732721577', '1453975484', '3027352986', '92245848', '894507159', '1937970808', '2925251320', '3840231238', '1947512427', '87962584', '1345571924', '972005658', '2683224058', '1106103765', '2446538932', '2554291574', '1623487913', '1686583866', '2529072432', '536949855', '3866658418', '473538275', '252715505', '3632090303', '1146356213', '4153312619', '1724902117', '2484041898', '3307793700', '3559017589', '3433938884', '1699259166', '149023715', '1860786983', '4024680171', '3801659300', '1676335981', '2869069067', '2921914002', '186373638', '1721503828', '1389088809', '3266414126', '1267505164', '4082601014', '210121672', '2844548954', '252200822', '4077852091', '751599722', '2200462415', '572783070', '3794166180', '3400896624', '3683968161', '3463879879', '3698731704', '1274017391', '3575219411', '1616971251', '1764880922', '2692716579', '2124851829', '2828912555', '1045343006', '3779036237', '4261187406', '306958166', '1020530176', '3084061084', '2412119621', '705138890', '2491808242', '1486542415', '151423868', '4005039699', '1446467556', '3899715231', '2393885244', '1247327810', '794747940', '428549163', '3758162883', '1652327247', '1935879458', '175933138', '920467258', '2665900065', '3087892616', '1430932461', '2224153099', '322567970', '1916422039', '4154677177', '2304610871', '2067543712', '3525619764', '3272550779', '1660590445', '259138075', '881346279', '94512525', '1605098821', '589729782', '2893363609', '2510844308', '106517927', '1113399408', '2988714006', '1463875601', '3896502134', '2970420751', '1076018285', '3658267924', '826943401', '754337333', '3200581311', '3284750825', '1793588465', '2750641926', '3870303496', '2580858981', '3297564047', '2382175616', '2266549530', '491161389', '2322243932', '573567288', '2188393190', '2539026679', '3212363005', '456705289', '2953099360', '3803058898', '566408952', '3758801899', '2683913641', '1517264651', '403202920', '3266931065', '732598129', '109813855', '460864556', '144722539', '78250244', '2435385686', '4111934872', '2759090369', '2928497983', '2610444710', '3312315763', '216226519', '1776393554', '3850950041', '202133593', '1370824124', '479081127', '1625043970', '4032059949', '3451289141', '4080944038', '2530883438', '380089411', '3006554485', '2504428394', '1262217378', '1845786756', '3609940860', '3536688266', '2962466426', '1706271997', '2932009302', '603768826', '3864594429', '2863056708', '2833450000', '963608104', '3889821387', '4117510220', '172828127', '1395553217', '894709654', '1106485422', '1619676916', '1933542442', '3506742304', '4135388683', '454957861', '1708484785', '1752607782', '2397776798', '3402209713', '2325254433', '530014408', '651935571', '1394896327', '3885475162', '1448614598', '1217136514', '1512723504', '1159994451', '697614185', '477927278', '4282742813', '3780075220', '344812468', '560362801', '3029657775', '357251280', '1480147250', '314870193', '2044667813', '6437434', '1465819696', '1217021374', '3841578300', '2692070381', '2150953857', '2975829844', '1324111481', '2583998307', '2696856891', '1969780767', '1476388256', '3222518399', '92157758', '2403415191', '3951780197', '2606383328', '1565011423', '3611020962', '3224555758', '2298040175', '1501679119', '2951622929', '4124620007', '1431039561', '2671536488', '3094078712', '382048545', '2249210117', '41252927', '2316735731', '3603751260', '1829117208', '667779361', '559618821', '2106690920', '2081433153', '555916900', '543117146', '2608367718', '2452904679', '4049703237', '1766992061', '919381911', '1354554247', '237764772', '2986124911', '124610391', '2311647821', '3603111897', '356240482', '448741271', '2922105732', '1516435079', '1775404950', '2955785263', '3927372874', '746576079', '2769292935', '2506208118', '1987345098', '1975529948', '2002146936', '318862099', '1217566813', '2017010225', '1634579219', '3503174392', '2042072178', '1589820024', '2204013412', '1206345604', '846069276', '1283564583', '1132785258', '1256540913', '3499523914', '3559606266', '3984042837', '3795482697', '3862634099', '3340530619', '3765117189', '3938958732', '2923173603', '1193557066', '2024846810', '1243481310', '2649026611', '647263111', '2412834294', '1542554878', '3548695965', '1002527123', '3173692483', '4063178352', '1848676985', '2460426930', '1571031797', '404595428', '1329952040', '3523744982', '2414905031', '3608297024', '1189547370', '2818648337', '65641389', '1448857745', '731731924', '1692430260', '161579832', '3124721005', '3332029000', '1980308275', '585602638', '637049052', '3696948957', '2098363618', '4188088089', '2485944097', '4000643494', '3663077452', '3451292933', '2829792984', '4207915079', '3679301219', '3508318746', '478399061', '2549121627', '3988877752', '3042677921', '3413988347', '3972013386', '2717619434', '3390104974', '1464985006', '525960636', '4141771222', '3634298859', '886419721', '2540137484', '1327015206', '2695813245', '1445820524', '1118969585', '2118381839', '690774443', '1162270076', '2525346233', '3206828033', '901955315', '426403871', '2036538169', '4225875840', '3703612480', '1095856197', '3174369333', '274750357', '803811392', '550528378', '2958912432', '1753430045', '618917454', '1307636352', '438718654', '2083377581', '1936167908', '766696037', '2312158323', '3701581814', '3971390766', '714156115', '1690615074', '2678142390', '1328882359', '4136833166', '3507025494', '2793371951', '708064207', '1330529419', '1163805498', '2144881822', '708004717', '3817102878', '2498438372', '4198698180', '2559837086', '3397562981', '146533907', '3323634350', '2006849293', '3637421376', '2650088968', '1567986481', '2956814829', '572892424', '219359052', '2214433187', '3825145895', '1447532589', '1709116685', '2981182600', '1572334348', '4284647357', '1601859970', '2795264631', '1275822727', '1786607352', '553678266', '890543862', '3900897464', '4092803548', '3075394600', '186124210', '2896431832', '2577247101', '1691794213', '939765972', '1817785599', '3851327907', '1625070066', '158767969', '635082373', '1516164055', '769238397', '610652577', '3455574126', '412401632', '2278877242', '2968986224', '2107263424', '2245475199', '1035826200', '1307632801', '3476135728', '1775142948', '447444721', '1462736290', '1370895168', '2732029489', '4026781236', '1404721754', '1770070953', '3315040094', '1178771563', '1510798104', '447287460', '979051762', '831161723', '2885684023', '2615804415', '1308089773', '1985254914', '938329092', '3175320123', '2341354339', '2791173792', '3281109970', '604179853', '3174177804', '3732169617', '3733102523', '25927492', '1926291871', '447655182', '963138294', '4186902667', '4115436468', '2322876084', '2287617625', '1616653882', '151502491', '1586611723', '2801788819', '1269035551', '1977406305', '1781691537', '4234138929', '2113532094', '269461758', '65357600', '3191231184', '2009645483', '2923324245', '517636552', '519705421', '1584496408', '490005260', '2876844499', '2860846405', '1093844070', '491095881', '3279689124', '2740101266', '188836861', '738076221', '295859751', '1294481466', '3508322706', '352549477', '897265684', '4090120152', '1572089933', '2320431814', '75326416', '131720132', '2402784975', '4248391618'}\n"
     ]
    }
   ],
   "source": [
    "type(uniqueEvents)\n",
    "print(uniqueEvents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "events = pd.read_csv('UniqueEvents.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52238789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3753704467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3429508261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>478310868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1371415647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     event_id\n",
       "0    52238789\n",
       "1  3753704467\n",
       "2  3429508261\n",
       "3   478310868\n",
       "4  1371415647"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     event_id\n",
      "0    52238789\n",
      "1  3753704467\n",
      "2  3429508261\n",
      "3   478310868\n",
      "4  1371415647\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pandas.core.indexing._iLocIndexer at 0x1a0d7f2710>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(events.head())\n",
    "type(events)\n",
    "# event.reshape()\n",
    "events.iloc(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "event_id\n"
     ]
    }
   ],
   "source": [
    "for event in events :\n",
    "    print(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13418, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
